OLI Grant: Quantification of Zooplankton Viability Using Image Analysis of Nonrigid Motion: An Approach for Automation of Critical Assays Used For Assessment of Ballast Water Treatment Technology Effectiveness

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February 1, 2006Grant Funded: 2006

With the identification of ballast water transfer as a major pathway
for introduction of invasive aquatic species within the global coastal
oceans and major freshwater systems has come the technological challenge
for neutralizing this pathway. To encourage ship-owners to participate
in the development of Ballast Water Treatment (BWT) systems and
to provide a vehicle for acquisition of data demonstrating efficacy
of various treatment strategies, the United States Coast Guard (USCG)
has initiated the Shipboard Technology Evaluation Program (STEP)
for onboard ballast water treatment. A requirement for entry into
STEP is that the proposed BWT system will likely meet Navigation
and Vessel Inspection Circular (NVIC) and International Maritime
Organization (IMO) standards for removal or inactivation of ballast
water organisms and development of a sound experimental program
that unequivocally and quantitatively demonstrates the level of
effectiveness of the installed system. After assessment of ~4 BWT
systems none has been admitted into STEP. Part of the problem is
that assessment of zooplankton viability, a critical assay, is conducted
using labor intensive manual microscope techniques that preclude
the necessary statistical rigor to prove that the BWT system under
study will meet required standards. It is essential that the zooplankton
viability assay be automated. We propose to develop a video data
recording system coupled with image analysis that will permit quantification
of zooplankton viability in approximately 1/10th the time required
for manual assays. Zooplankton motility is the descriptor for viability
(as it is in the manual assays). The project will entail development
of a dark field illuminated stage that optimally presents the zooplankton
samples for video recording movement responses after global electro-
or piezoelectric stimulation and development of the image analysis
routines for enumeration and viability scoring of the zooplankton
samples. For detection of motion we will explore two approaches:
1) Modeling the zooplankton as single deformable objects, where
changing properties of periphery-hugging "snakes" are
quantified as a feature vector which is both scale- and rotation-invariant.
If the movement of live zooplankton results in feature vectors that
are markedly different from those of dead zooplankton, the two feature
classes can be distinguished by a pattern classifier; 2) In the
situation where the differences between the two classes are more
subtle, we will model the zooplankton as articulated objects, consisting
of limbs and joint angles. The new feature sets will consist of
the feature vectors of the deformable limbs as well as all the joint
angles. This will provide a more subtle description of the articulated
motion and, consequently, the resulting pattern classifier will
be more effective.

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